LDM-Morph: Latent diffusion model guided deformable image registration
This work addresses deformable image registration for medical imaging, offering incremental improvements in accuracy and topology preservation for cardiac image analysis.
The authors tackled the problem of limited semantic information and deformation folding in deep learning-based deformable image registration by proposing LDM-Morph, which integrates latent diffusion model features and a hierarchical metric, resulting in improved accuracy and topology preservation over state-of-the-art methods on cardiac image datasets.
Deformable image registration plays an essential role in various medical image tasks. Existing deep learning-based deformable registration frameworks primarily utilize convolutional neural networks (CNNs) or Transformers to learn features to predict the deformations. However, the lack of semantic information in the learned features limits the registration performance. Furthermore, the similarity metric of the loss function is often evaluated only in the pixel space, which ignores the matching of high-level anatomical features and can lead to deformation folding. To address these issues, in this work, we proposed LDM-Morph, an unsupervised deformable registration algorithm for medical image registration. LDM-Morph integrated features extracted from the latent diffusion model (LDM) to enrich the semantic information. Additionally, a latent and global feature-based cross-attention module (LGCA) was designed to enhance the interaction of semantic information from LDM and global information from multi-head self-attention operations. Finally, a hierarchical metric was proposed to evaluate the similarity of image pairs in both the original pixel space and latent-feature space, enhancing topology preservation while improving registration accuracy. Extensive experiments on four public 2D cardiac image datasets show that the proposed LDM-Morph framework outperformed existing state-of-the-art CNNs- and Transformers-based registration methods regarding accuracy and topology preservation with comparable computational efficiency. Our code is publicly available at https://github.com/wujiong-hub/LDM-Morph.